1. Field of the Invention
The present invention relates in general to digital image processing. Specifically, the present invention relates to methods for improving image resolution and improving image data transmission in medical imaging, movies and video games, teleimage, and any other areas involving digital image acquisition and transmission. More specifically, the present invention provides methods for increasing image resolution—spatially and/or temporally—and methods for transmitting image data using back pixelation, a technique that involves data processing and reconstruction of overlaid images from multiple acquisitions or multiple sampling.
2. Description of the Related Art
In the contemporary digital age, digital image processing is taking an increasingly essential part in medical diagnostics, telecommunication, entertainment (e.g. movies and video games), and scientific research and development. Resolution of digital images directly affects their quality and application. Improving image resolution—spatially and/or temporarily—is a continuous challenge to the engineers, researchers, and practitioners alike. Improving image resolution without increasing the capacity of hardware imaging acquisition instruments is particularly challenging. Magnetic resonance imaging (MRI), for example, is such an area where these challenges are keenly felt by clinicians and medical engineers.
MRI measures the radio frequency response (“signal”) of the target tissue to radio frequency waves generated by the magnetic resonance (MR) scanner; such response or signal is delineated by a MR pulse sequence. The pulse sequence determines the image contrast, the speed of image acquisition, and the spatial resolution of the resulting MR image. The maximal spatial and temporal resolutions are limited by the strength of the magnetic field and the scanner's gradient hardware.
High resolution scanning is desired for better morphologic depiction and lesion characterization. Low spatial resolution on one or more dimensions (i.e. large pixel or voxel size) may cause a partial volume effect that results in poor differentiation of structures. That is, clinically relevant information may be masked if a lesion is mixed with other tissue(s) in the space represented by a single pixel or voxel. See, e.g., Schreiner, S.; et al, Journal of Computer Assisted Tomography 20(1):56-67. In order to depict a lesion at an accepted confident level thereby preventing diagnostic errors, the pixel or voxel size needs to be no larger than half the size of the lesion. See, Id. High spatial resolution for acquiring images is thus vital in these situations.
Clinical imaging such as MRI also requires a balance between temporal resolution and spatial resolution, the later represented by the pixel or voxel size and the former the scan time. Fast acquisition or high temporal resolution is important for reducing the length of the exam time and the overall exam cost; and more importantly, it is critical in assessing dynamic changes and monitoring structures in real-time imaging, especially during the use of contrast agents for tissue and organ perfusion imaging. However, fast imaging is often performed at the expense of spatial resolution. That is, for example, imaging a fixed field of view (FOV) with a 128×128 matrix may be performed more quickly than with a 256×256 matrix, but the resulting lower spatial resolution information of the 128×128 acquisition may be insufficient for confident diagnosis of structures. The 256×256 acquisition—or the spatial resolution achieved thereby—may be required for structural identification and depiction. Yet, a 256×256 image requires a longer acquisition period, yielding a lower temporal resolution, and hence its diagnostic utility for dynamic or real time imaging may be limited, e.g., in the cases where contrast media enhancement is evaluated for medical diagnosis.
One further consideration is that of signal-to-noise ratio (S/N), which must also be sufficient for adequate differentiation of certain structural features. In the case of the 256×256 matrix, for example, the spatial resolution achieved may be sufficient for depiction of a target structure but the S/N of the relatively small voxel or pixel size may be insufficient for actual visualization of the structures. Whereas, a 128×128 matrix (with a larger pixel or voxel size) may be performed quicker and yields a higher S/N per pixel or per voxel. In general, therefore, high spatial resolution, high temporal resolution, and high S/N are all preferable for MRI; however, they often represent competing factors in image acquisition that call for appropriate balancing.
A high S/N is necessary for visualizing small structures in MRI. S/Ns may be improved by a variety of methods such as increasing the number of excitation averages, decreasing the receiver bandwidth, or increasing the acquisition repetition time. These methods improve S/N but slow image acquisition, thus result in decreased temporal resolution. On the other hand, S/N may be compromised by decreasing pixel or voxel size, which marks improved spatial resolution.
The interplay of spatial resolution, temporal resolution, and S/N, therefore, poses significant challenge for optimizing all the parameters to achieve desired imaging results in different applications. One way to increase spatial resolution is to apply higher magnetic field strength (e.g. 1.5, 2, 3, and 3 Tesla versus 0.035 or 0.3 Tesla) and high performance gradient sets (e.g. 40-60 mT/m versus 5-10 mT/m); these hardware parameters dictate achievable matrix sizes (x and y dimensions) and slice thickness (z dimension). Increasing hardware capacity as such often associates with high cost.
Certain post-processing techniques, such as zero-filled interpolation and voxel shift interpolation, have been proposed to address spatial resolution related partial volume effect problem in some applications. See, e.g., Du Y. et al., JMRI September/October 1994 p. 733-741. However, these filtering methods do not inherently improve spatial resolution; the method of Du et al., for example, only interpolates intermediate voxels. These methods can not, nor can other existing image processing or restoration methods, accurately determine a signal that would be returned from a portion of tissues with a size smaller than one pixel or one voxel.
Additionally, the size of a digital image in application areas such as medical imaging is typically fairly large such that the transmission of such images poses a significant challenge on transmission capacity. To achieve satisfactory resolution after transmission is a lasting battle for the engineers and researchers alike. Certain data segmentation, compression, or reduction techniques have been used (e.g., preserving every fourth point of the image). But, they may result in voids in the transmitted image and therefore may not be desirable in some situations where high image integrity is required.
There is therefore a need for methods that effectively improve and optimally balance spatial and temporal resolution of digital images on acquisition and efficiently transmit the same at a satisfactory resolution in various application areas, especially those that do not demand enhancement of imaging acquisition capacities of equipment and/or hardware.